Organizations worldwide are facing a stark reality: there aren’t enough developers to fill the demand for them. That gap will only increase in the coming years.
3 Data Tips to Keep Developers Happy
- Give developers direct access to data when and where they need it.
- Analyze data in real time so developers can spot and fix issues as they occur.
- Implement automatic discovery to save developers time.
In the United States, there will be more than 160,000 open positions for software developers and related jobs each and every year through the end of the decade, according to the Bureau of Labor Statistics. From 2021 to 2031, employment in this industry will skyrocket 25 percent, while the average growth rate for all occupations is a mere 5 percent. Over the next several years, competition for developers will be fierce, requiring organizations to do more to recruit and retain these workers.
Why Developers Face Burnout
One of the biggest challenges developers face is burnout. In fact, one survey found that an alarming 83 percent of software engineers report burnout. Crucially, about a third of developers cited inefficiencies on the job as a key contributor.
Not coincidentally, developers are searching for new opportunities. Nearly three-quarters of developers are actively looking for new roles or would be open to new positions, according to a Stack Overflow survey.
To be sure, developers will always face some difficulties in the workplace. Developing software is an incredibly complex, time-intensive profession. There are often tight deadlines and unexpected hurdles in addition to the pressure to perform and keep innovating.
But companies can quickly improve an often-overlooked factor that greatly contributes to developer burnout and turnover. That factor relates to observability.
Why Observability Matters
In essence, observability is about checking if apps and systems are working as intended or if there are any bugs or bottlenecks.
Historically, observability hasn’t been a major issue for developers. While challenging, it was manageable to pool and analyze data in order to assess app and system performance and ensure user experiences were fast and reliable, or if not, discern the root cause of performance issues.
But the quantity of this observability data has exploded in recent years. There are more users than ever before and the use of the cloud and shift toward microservices has surged, leading to exponentially more production environments being created every moment. Applications also have more interfaces and functions in the form of APIs, each one generating data and therefore requiring collection and analysis.
Developers should have automatic access to their data, whenever and wherever they need it.
One might suspect that more data would help developers uncover more issues and improve the user experience. After all, more data points can provide more clues as to what’s really going on.
But the result has been quite the opposite. Now, when something goes awry with a service, developers are stuck wading through troves of data just to identify the root cause of the issue. It’s like trying to find a needle in a haystack and they need a way to identify the root source of problems more expeditiously.
The situation is made even more complicated by the fact that developers often don’t have access to the data. Frequently, organizations are forced to discard valuable data in order to meet budget requirements. There must be a way for them to effectively have an eye on all data, without blowing through storage budgets.
Moreover, developers often need to work with the operations team to gain access to this data, making the process even more time-consuming and cumbersome when something goes wrong. Developers should have automatic access to their data, whenever and wherever they need it.
These unnecessary inefficiencies are an enormous headache for developers and prevent them from using their skill set to the fullest.
How to Flip the Observability Paradigm
The good news is that we have plenty of tools at our disposal that can cut out unnecessary inefficiencies and stressors, allowing developers to focus on innovating and creating software.
This requires radically transforming processes, like observability. Traditionally, all data generated gets routed into a central depository for analysis. In theory, this makes sense: all of the data is in one place for review.
But the reality is far less glamorous. To start, this massive influx of data slows down systems dramatically, taking too much time when a performance issue may be costing an organization hundreds of thousands per minute. Second, sifting through all the information becomes a monotonous slog, which can slow down incident response times. Additionally, storing all the data in a hot, searchable repository can be expensive.
The remedy? Move data analysis upstream. Specifically, rather than looking through all the data at once, data should be analyzed in real time, from its point of origin. That helps developers identify issues at their source as soon as they occur, not amid a hodgepodge of information.
Use Real-Time Analytics
Thanks to advancements in the cloud and microservices, the moment an application goes live or an instance is created, real-time analytics can come into play and start generating insights. This process is referred to as automatic discovery and can remove a lot of unnecessary stress and wasted time.
Rather than manually adding each and every new instance or service to observability initiatives, this process can happen automatically, meaning data can start being captured and analyzed right away versus leaving developers in the lurch until the new instance or service is accounted for.
This can be a huge advantage, particularly for major company moments such as the launch of a new app. Often, performance issues come up quickly after a launch, and without automatic discovery, developers may become aware of this too late, potentially disappointing huge swaths of users. With automatic discovery, developers can avoid this unfortunate scenario and quickly identify and resolve issues, maintaining user interest and enthusiasm.
Talented developers are the linchpins of success for many organizations. Leaders need to take steps to ensure their developers, both current and forthcoming, have the support and resources they need to perform at their best. Crucially, that means embracing new ways of doing things to reduce inefficiencies and avoidable sources of stress. Enabling a better handle on observability data is one key way to do this.
